Siuly / Xing / Li Health Information Science

13th International Conference, HIS 2024, Hong Kong, China, December 8–10, 2024, Proceedings
Erscheinungsjahr 2025
ISBN: 978-981-965597-7
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 340 Seiten

Reihe: Lecture Notes in Computer Science

This book LNCS 15336 constitutes the refereed proceedings of the 13th International Conference on Health Information Science, HIS 2024, held in Hong Kong, China, during December 8-10, 2024.

The 18 full papers and 11 short papers were carefully reviewed and selected from 59 submissions.  The scope of the conference includes:

(1) medical/health/biomedicine information resources, such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, and optimize the use of information in the health domain;

(2) data management, data mining, and knowledge discovery, all of which play a key role in decision-making, management of public health, examination of standards, privacy and security issues;

(3) computer visualization and artificial intelligence for computer-aided diagnosis;

(4) development of new architectures and applications for health information systems.

Zielgruppe

Research

Autoren/Hrsg.

Siuly, Siuly

Xing, Chunxiao

Li, Xiaofan

Zhou, Rui

Inhaltsverzeichnis

Artificial Intelligence in Enhancing Electronic Health Record Systems: A Comprehensive Survey.- Complex Query Optimization in Sepsis Data Using Learned Index with Data Transformation.- A Novel Feature Selection Framework Based on Large Language Models.- LTGAN: Multi-label Time-series GAN with Constraints for Electronic Health Records Generation.- Multi-condition Join Query Optimization For Disease-specific Data Exploration Based On Data Lake.- Multi-modal Medical Data Management Platform Based on Data Lake.- Comparative Study of Machine Learning Algorithms for IoT Cyber Threat Detection in Healthcare Information Systems.- Myocardial Band Transformer Network for Detecting Myocardial Ischemia in 2D Echocardiography.- Deep Learning-Enabled Electronic Health Records for Accurate Diagnosis of Cardiovascular Diseases: A Retrospective, Prospective, Diagnostic Study.- V-GnNet: Voxel and Graph Node Based Network for Continuously Consistent Artery and Vein Classification in Non-contrast CT Images.- Multilabel Classification for Cancer Subtypes.- TG.Net:A deep learning model combining attention mechanism for effective prediction of multi stages of Chronic Kidney Disease.- A Deep Transfer Learning Approach for Predicting Diabetes Complications Using Genomic Data.- Using machine learning to predict the risk severity of late effects of a childhood rhabdomyosarcoma survivors.- Predicting C-section Outcomes among Bangladeshi Women: A Comparative Study of Machine Learning Techniques.- Exploring Relations between Depression and Cognitive Impairment.- Investigating Roles of Immune Functions in Depression by Using Knowledge Graph Approach.- EEG Sleep Classification Based on Fourier-Bessel Technique Coupled with LS-SVM.- An AI driven framework for EEG based-BCI technology.- Perceptions of Academic Performance and Mental Health Among Pharmacy Students Across Different Learning Modalities.- Epilepsy Detection from Weighted EEG Graph (WEG) using Novel Feature- Normalized Weighted Forgotten Topological Index (NWFT-index).- Extended Knowledge Graphs of Depression.- IBDR-Net: A Computerized Framework for Irregular Brain Signal Data Recognition.- Physical Human Activity Recognition Based on Spectral Graph Wavelet Transforms Integrated with Machine Learning Model.- Robust Approach for Human Activity Recognition Using Decomposition Technique Based Machine Learning Models.- The Impact of COVID-19 and the Ukrainian war on the share market index for the Australian Financial sector.- The Impact of COVID-19 on Global GMAT Test-Taking Patterns: A Statistical Analysis.- A Geriatric Disease Medical Consultation System Based on Multi-Agent Architecture and Knowledge Graph.- Impact of social media on heart health-related behaviours in health promotion interventions: sentiment analysis of tweets linked to the American Heart Month campaigns.